Spatially Adaptive Regularization in Image Segmentation

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median...
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Autor*in:	Laura Antonelli [verfasserIn] Valentina De Simone [verfasserIn] Daniela di Serafino [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	image segmentation spatially adaptive regularization nonsmooth optimization split bregman method

Übergeordnetes Werk:	In: Algorithms - MDPI AG, 2008, 13(2020), 9, p 226
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:9, p 226

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/a13090226

Katalog-ID:	DOAJ015364712

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520			\|a We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
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650		4	\|a spatially adaptive regularization
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allfieldsSound	10.3390/a13090226 doi (DE-627)DOAJ015364712 (DE-599)DOAJ63c5a79fc1294afcac7fb87b788ea7a4 DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Laura Antonelli verfasserin aut Spatially Adaptive Regularization in Image Segmentation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach. image segmentation spatially adaptive regularization nonsmooth optimization split bregman method Industrial engineering. Management engineering Electronic computers. Computer science Valentina De Simone verfasserin aut Daniela di Serafino verfasserin aut In Algorithms MDPI AG, 2008 13(2020), 9, p 226 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:13 year:2020 number:9, p 226 https://doi.org/10.3390/a13090226 kostenfrei https://doaj.org/article/63c5a79fc1294afcac7fb87b788ea7a4 kostenfrei https://www.mdpi.com/1999-4893/13/9/226 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2020 9, p 226
language	English
source	In Algorithms 13(2020), 9, p 226 volume:13 year:2020 number:9, p 226
sourceStr	In Algorithms 13(2020), 9, p 226 volume:13 year:2020 number:9, p 226
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institution	findex.gbv.de
topic_facet	image segmentation spatially adaptive regularization nonsmooth optimization split bregman method Industrial engineering. Management engineering Electronic computers. Computer science
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authorswithroles_txt_mv	Laura Antonelli @@aut@@ Valentina De Simone @@aut@@ Daniela di Serafino @@aut@@
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callnumber-first	T - Technology
author	Laura Antonelli
spellingShingle	Laura Antonelli misc T55.4-60.8 misc QA75.5-76.95 misc image segmentation misc spatially adaptive regularization misc nonsmooth optimization misc split bregman method misc Industrial engineering. Management engineering misc Electronic computers. Computer science Spatially Adaptive Regularization in Image Segmentation
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topic	misc T55.4-60.8 misc QA75.5-76.95 misc image segmentation misc spatially adaptive regularization misc nonsmooth optimization misc split bregman method misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_unstemmed	misc T55.4-60.8 misc QA75.5-76.95 misc image segmentation misc spatially adaptive regularization misc nonsmooth optimization misc split bregman method misc Industrial engineering. Management engineering misc Electronic computers. Computer science
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title	Spatially Adaptive Regularization in Image Segmentation
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title_auth	Spatially Adaptive Regularization in Image Segmentation
abstract	We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
abstractGer	We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
abstract_unstemmed	We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.
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title_short	Spatially Adaptive Regularization in Image Segmentation
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